Hajra Naz
OpenAI’s chief global affairs officer Chris Lehane believes the 2028 presidential election could mark a defining moment for AI. He wants politicians to start developing policies that address public concerns. He argues leaders should not simply pay lip service to anti-AI sentiment or data center backlash.
In a new essay, Lehane compares AI’s economic potential to President Franklin D. Roosevelt’s New Deal. That 1930s program created federal reforms after industrialization and the Great Depression shattered the old economy.
He observes that Americans today feel anxious about losing control over their economic futures. He urges politicians to find a “compelling third path” that lets people share in AI’s benefits. He outlines five policy priorities. These cover worker protections, education, AI safety, and data center development.
The essay follows a stunning Democratic victory in the midterm elections. Democrats flipped control of Congress amid voter frustration over the cost of living. Kate Brennan, who leads AI policy at the AI Now Institute, spoke to TechCrunch. She noted that Lehane’s rhetoric marks a shift from OpenAI’s previous policy agenda. Last year, the company focused on accelerating AI development, cutting regulations, and expanding infrastructure.
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Many of Lehane’s proposals encourage wider AI adoption. They support the infrastructure expansion that companies like OpenAI need to grow. The original New Deal protected workers and constrained corporate power. It is less clear how Lehane’s vision holds AI companies accountable for economic disruption.
“Invoking a New Deal metaphor is to invoke state capacity to actually benefit the public,” Brennan said. “This is written by a private company that is trying to advocate for policies for its own benefit. It is ahistorical at best and harmful at worst.”
AI was not a dominant issue in the midterms. However, opposition to data centers emerged as a bipartisan concern in several races. Lehane may be right that AI will become a larger topic over the next two years.
Affordability is shaping up as a key issue for the 2028 election. Americans continue to struggle with inflation, tariffs, and rising living costs. AI could become a bigger part of that debate. Voters worry about data centers raising electricity bills, AI replacing jobs, and wealth flowing only to tech giants.
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Lehane acknowledges many of these concerns. However, he offers few concrete proposals to prevent wealth concentration. Instead, he pitches AI as a path to prosperity for those who embrace it.
Consider his tax code proposals. He suggests incentives for learning AI skills, easier capital access for entrepreneurs, and new profit-sharing models. These are not binding requirements that guarantee workers share in AI wealth. They also do not force companies to pay for worker displacement.
“I was struck by how much of this policy is for a future…that is already predicated on mass AI adoption and AI benefiting society, which we have yet to see,” Brennan said. “All of it is predicated on benefiting from AI’s upside.”
Brennan suggested alternative policies would make more sense. She pointed to revised corporate tax rates, tax incentives for hiring humans over automation, and rules to curb market power.
Lehane emphasizes expanding economic opportunities rather than limiting potential harms. This approach extends to data centers. He acknowledges local concerns while highlighting construction jobs and tax revenue. He then warns that data center moratoriums could jeopardize America’s lead over China. He even compares that stance to the 2002 vote authorizing the Iraq war.
Lehane also makes child safety and education central to his agenda. He stresses age-appropriate protections and safeguards around AI companions for minors. He warns against keeping AI out of classrooms. He argues that banishing AI could widen inequality between wealthy students and rural children. However, that argument assumes AI improves learning. Critics note that claim remains unproven and deserves deeper study.
Lehane also calls for enforceable safety standards, independent evaluations, public oversight, and kill switches to shut down AI systems. Yet recent studies show that OpenAI and other labs have not published complete plans for handling rogue models.
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Brennan called these stances “disingenuous.” She pointed to OpenAI’s history of opposing safety legislation. In 2024, OpenAI opposed California’s AI safety bill, SB 1047. That bill would have mandated risk evaluations and shutdown capabilities. Last year, OpenAI also sought to limit state regulation, claiming it hurt innovation. The company has since backed some state safety laws and expressed openness to independent evaluators.
Lehane further argues that humans must maintain control over major AI decisions. However, that raises questions about what human control means in practice. Humans are increasingly required to use AI across education, work, healthcare, and government.
Throughout the essay, Lehane frames AI as a tool that restores economic agency to Americans. Yet he ties middle-class success to an economy shaped by companies like OpenAI. Whether these policies redistribute power or entrench tech dominance remains to be seen.
